Optimization approaches for graphical layout design

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2022-11-11
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Date

2022

Department

Tietoliikenne- ja tietoverkkotekniikan laitos
Department of Communications and Networking

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Mcode

Degree programme

Language

en

Pages

98 + app. 100

Series

Aalto University publication series DOCTORAL THESES, 153/2022

Abstract

The graphical user interface (GUI) is the most frequently used method of communicating with computers. These interfaces employ graphical components rather than either pure command lines or natural language for the purpose of interaction. A well-designed GUI is critical for improving the interaction's efficacy and utility. Currently, the process of developing GUIs is primarily a manual one that is time-consuming and difficult, and the final product depends greatly on designer competence. Furthermore, the large design space of alternative designs complicates manual design. For example, the number of distinct positions possible for placing five elements on a common canvas of 1024 X 768 pixels (for simplicity, suppose the canvas is divided into 32 X 24 pixels) is approximately 2.6e+14. Whereas analyzing these solutions manually would demand large amounts of time and effort, the thesis examines the alternative of developing computational methods (e.g., mathematical models, deep learning, and heuristic algorithms) and their integration into the design process, for purposes of addressing the inherent complexity of manual design. Several models and algorithms are proposed that automate aspects of the GUI-design process.In one method, a grid operator is presented to a non-dominated-sorting genetic algorithm for graphical layout problems. These grid operators create some vertical and horizontal lines using the fixed elements. The remaining unfixed elements are then inserted between these lines. This procedure ultimately results in the satisfaction of overlap-related constraints and better element alignment, thus fulfilling one of the main objectives of a designer organizing layout elements. The second method developed, which involves a deep neural network estimating Web pages' visual appeal, could serve objective-function approximation that informs evaluating a given design's aesthetics. Results from a deep neural network developed in the doctoral project attest to the ensuing model's ability to predict the ratings that people with diverse demographic backgrounds would give the Web page shown. For the last method, empirical evaluation of a novel integer-programming formulation for menu systems was designed and implemented. The optimized design exhibited increased efficiency: user interactions were around 25% quicker than with non-optimized commercial designs. Along with the project's contribution, the thesis discusses the future work required for solving GUI problems from both a theoretical and a practical standpoint.

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Supervising professor

Oulasvirta, Antti, Prof., Aalto University, Department of Communications and Networking, Finland

Keywords

optimization, graphical design

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Parts

  • [Publication 1]: Morteza Shiripour, Niraj Ramesh Dayama, and Antti Oulasvirta. Grid-based genetic operators for graphical layout generation. Proceedings of the ACM on Human–Computer Interaction, Volume 5, Article 208, pp. 1–30, June 2021.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202106167379
    DOI: 10.1145/3461730 View at publisher
  • [Publication 2]: Niraj Ramesh Dayama, Morteza Shiripour, Antti Oulasvirta, Evgeny Ivanko, and Andreas Karrenbauer. Foraging-based optimization of menu systems. International Journal of Human–Computer Studies, Volume 151, July 2021.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202103102275
    DOI: 10.1016/j.ijhcs.2021.102624 View at publisher
  • [Publication 3]: Luis A. Leiva, Morteza Shiripour, and Antti Oulasvirta. Modeling how different user groups perceive webpage aesthetics. Universal Access in the Information Society, pp. 1–8, August 2022.
    DOI: 10.1007/s10209-022-00910-x View at publisher
  • [Publication 4]: Antti Oulasvirta, Niraj Ramesh Dayama, Morteza Shiripour, Maximilian John, and Andreas Karrenbauer. Combinatorial optimization ofgraphical user interface designs. Proceedings of the IEEE, Volume 108, pp. 434–464, February 2020.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202004282919
    DOI: 10.1109/JPROC.2020.2969687 View at publisher

Citation

Permanent link to this item

https://urn.fi/URN:ISBN:978-952-64-0991-7

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[diss] Sähkötekniikan korkeakoulu / ELEC